class_docstr: Inception v3 model from
  `"Rethinking the Inception Architecture for Computer Vision"
  <http://arxiv.org/abs/1512.00567>`_ paper.
  
  Parameters
  ----------
  classes : int, default 1000
      Number of classification classes.
  norm_layer : object
      Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
      Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
  norm_kwargs : dict
      Additional `norm_layer` arguments, for example `num_devices=4`
      for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
class_docstr_tok: ["Inception","v3","model","from","`","``","Rethinking","the","Inception","Architecture","for","Computer","Vision","''","<","http",":","\/\/arxiv.org\/abs\/1512.00567",">","`","_","paper",".","Parameters","--","--","--","--","--","classes",":","int",",","default","1000","Number","of","classification","classes",".","norm_layer",":","object","Normalization","layer","used","(","default",":",":","class",":","`","mxnet.gluon.nn.BatchNorm","`",")","Can","be",":","class",":","`","mxnet.gluon.nn.BatchNorm","`","or",":","class",":","`","mxnet.gluon.contrib.nn.SyncBatchNorm","`",".","norm_kwargs",":","dict","Additional","`","norm_layer","`","arguments",",","for","example","`","num_devices=4","`","for",":","class",":","`","mxnet.gluon.contrib.nn.SyncBatchNorm","`","."]
code: def __init__(
      self,
      classes=1000,
      norm_layer=BatchNorm,
      norm_kwargs=None,
      partial_bn=False,
      **kwargs,
  ):
      super(Inception3, self).__init__(**kwargs)
      # self.use_aux_logits = use_aux_logits
      with self.name_scope():
          self.features = nn.HybridSequential(prefix="")
          self.features.add(
              _make_basic_conv(
                  channels=32,
                  kernel_size=3,
                  strides=2,
                  norm_layer=norm_layer,
                  norm_kwargs=norm_kwargs,
              )
          )
          if partial_bn:
              if norm_kwargs is not None:
                  norm_kwargs["use_global_stats"] = True
              else:
                  norm_kwargs = {}
                  norm_kwargs["use_global_stats"] = True
  
          self.features.add(
              _make_basic_conv(
                  channels=32,
                  kernel_size=3,
                  norm_layer=norm_layer,
                  norm_kwargs=norm_kwargs,
              )
          )
          self.features.add(
              _make_basic_conv(
                  channels=64,
                  kernel_size=3,
                  padding=1,
                  norm_layer=norm_layer,
                  norm_kwargs=norm_kwargs,
              )
          )
          self.features.add(nn.MaxPool2D(pool_size=3, strides=2))
          self.features.add(
              _make_basic_conv(
                  channels=80,
                  kernel_size=1,
                  norm_layer=norm_layer,
                  norm_kwargs=norm_kwargs,
              )
          )
          self.features.add(
              _make_basic_conv(
                  channels=192,
                  kernel_size=3,
                  norm_layer=norm_layer,
                  norm_kwargs=norm_kwargs,
              )
          )
          self.features.add(nn.MaxPool2D(pool_size=3, strides=2))
          self.features.add(_make_A(32, "A1_", norm_layer, norm_kwargs))
          self.features.add(_make_A(64, "A2_", norm_layer, norm_kwargs))
          self.features.add(_make_A(64, "A3_", norm_layer, norm_kwargs))
          self.features.add(_make_B("B_", norm_layer, norm_kwargs))
          self.features.add(
              _make_C(128, "C1_", norm_layer, norm_kwargs)
          )
          self.features.add(
              _make_C(160, "C2_", norm_layer, norm_kwargs)
          )
          self.features.add(
              _make_C(160, "C3_", norm_layer, norm_kwargs)
          )
          self.features.add(
              _make_C(192, "C4_", norm_layer, norm_kwargs)
          )
          self.features.add(_make_D("D_", norm_layer, norm_kwargs))
          self.features.add(_make_E("E1_", norm_layer, norm_kwargs))
          self.features.add(_make_E("E2_", norm_layer, norm_kwargs))
          self.features.add(nn.AvgPool2D(pool_size=8))
          self.features.add(nn.Dropout(0.5))
  
          self.output = nn.Dense(classes)
  
file_path: gluon-cv/gluoncv/model_zoo/inception.py
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.contrib.nn import HybridConcurrent
label_desc: Use image node_id 1 to create a new Inception3 object from inherited base classes: HybridBlock with example: obj = Inception3(classes, norm_layer, norm_kwargs, partial_bn)